AI in Banking: How NLB Uses GenAI Securely and at Scale
How can AI applications be deployed securely, transparently, and efficiently in a highly regulated banking environment? Learn how NLB is working with Adastra to implement AI use cases, from an internal consultant for regulated documents to support for Anti-Money Laundering processes.
Together with Adastra, NLB has developed AI solutions that help employees handle complex regulatory processes more efficiently and securely. One key use case is an internal AI assistant designed to simplify access to information across approximately 1,500 regulated internal documents.
Another use case focuses on processing Anti-Money Laundering alerts.
The recording demonstrates why production-ready AI in banking goes far beyond traditional chatbots and how it can effectively support complex, regulated processes. Regulatory requirements mean that data quality, document versions, access rights, security, audit trails, and governance need to be considered from the outset.
Using concrete examples, Aleš Gorišek explains why moving from a Proof of Concept (PoC) to a production-ready solution depends less on the model itself and more on integration, architecture, access control, trust, and a clear platform approach. Shadow AI, switching between models, cost control, LLM gateways, and European data residency also play a critical role.
Key Takeaways from the Webinar
GenAI in Banking
Production readiness requires more than a powerful model: High accuracy alone is not enough. Data quality, accuracy, governance, and trust need to be addressed from the outset. NLB is developing an AI Consultant together with Adastra.
Agentic AI
Combining automation with clear controls: NLB is exploring Agentic AI for complex processes such as Anti-Money Laundering alerts while maintaining human decision-making authority, auditability, access control, and guardrails.
From PoC to a Production-Ready AI Platform
The underlying architecture is critical: The success of AI applications depends not only on the model, but also on integration, security, authentication, and authorization.
Controlling Shadow AI Without Slowing Innovation
A central AI platform can provide secure access to models, cost transparency, and regulatory control without preventing employees from using AI creatively across the organization.
Who Is This Recording Most Relevant For?
This recording is aimed at professionals and decision-makers across Banking, Financial Services, IT, Data, Compliance, and Risk Management who want to understand how AI can be deployed securely and efficiently in regulated enterprise environments.
- IT, Data, AI, and platform leaders at banks and regulated organizations looking to implement GenAI and Agentic AI use cases securely, at scale, and with appropriate controls.
- Compliance, Risk, Security, and Governance teams seeking to understand how AI systems interact with regulatory requirements, data residency, audit trails, access controls, and Shadow AI risks.
- Business and Operations leaders looking to identify practical AI use cases for internal knowledge processes, document analysis, AML processes, or decision support in regulated environments.
Learn how NLB develops and deploys Generative AI and Agentic AI efficiently in a highly regulated environment, and why a secure, controlled AI platform is key to innovation, efficiency, and trust.
